Drug Safety

, Volume 36, Supplement 1, pp 107–121 | Cite as

Empirical Performance of the Calibrated Self-Controlled Cohort Analysis Within Temporal Pattern Discovery: Lessons for Developing a Risk Identification and Analysis System

  • G. Niklas Norén
  • Tomas Bergvall
  • Patrick B. Ryan
  • Kristina Juhlin
  • Martijn J. Schuemie
  • David Madigan
Original Research Article

Abstract

Background

Observational healthcare data offer the potential to identify adverse drug reactions that may be missed by spontaneous reporting. The self-controlled cohort analysis within the Temporal Pattern Discovery framework compares the observed-to-expected ratio of medical outcomes during post-exposure surveillance periods with those during a set of distinct pre-exposure control periods in the same patients. It utilizes an external control group to account for systematic differences between the different time periods, thus combining within- and between-patient confounder adjustment in a single measure.

Objectives

To evaluate the performance of the calibrated self-controlled cohort analysis within Temporal Pattern Discovery as a tool for risk identification in observational healthcare data.

Research Design

Different implementations of the calibrated self-controlled cohort analysis were applied to 399 drug-outcome pairs (165 positive and 234 negative test cases across 4 health outcomes of interest) in 5 real observational databases (four with administrative claims and one with electronic health records).

Measures

Performance was evaluated on real data through sensitivity/specificity, the area under receiver operator characteristics curve (AUC), and bias.

Results

The calibrated self-controlled cohort analysis achieved good predictive accuracy across the outcomes and databases under study. The optimal design based on this reference set uses a 360 days surveillance period and a single control period 180 days prior to new prescriptions. It achieved an average AUC of 0.75 and AUC >0.70 in all but one scenario. A design with three separate control periods performed better for the electronic health records database and for acute renal failure across all data sets. The estimates for negative test cases were generally unbiased, but a minor negative bias of up to 0.2 on the RR-scale was observed with the configurations using multiple control periods, for acute liver injury and upper gastrointestinal bleeding.

Conclusions

The calibrated self-controlled cohort analysis within Temporal Pattern Discovery shows promise as a tool for risk identification; it performs well at discriminating positive from negative test cases. The optimal parameter configuration may vary with the data set and medical outcome of interest.

References

  1. 1.
    Edwards IR, Lindquist M, Wiholm BE, Napke E. Quality criteria for early signals of possible adverse drug reactions. Lancet. 1990;336(8708):156–8.PubMedCrossRefGoogle Scholar
  2. 2.
    Rawlins MD. Spontaneous reporting of adverse drug reactions. I: the data. Brit J Clin Pharmacol. 1988;26(1):1–5.CrossRefGoogle Scholar
  3. 3.
    Meyboom RH, Lindquist M, Egberts AC. An ABC of drug-related problems. Drug Saf. 2000;22(6):415–23.PubMedCrossRefGoogle Scholar
  4. 4.
    Harpaz R, DuMouchel W, LePendu P, Bauer-Mehren A, Ryan P, Shah NH. Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clin Pharmacol Ther. 2013;93(6):539–46.PubMedCrossRefGoogle Scholar
  5. 5.
    Public Law 110-85: Food and Drug Administration Amendments Act of 2007. 2007.Google Scholar
  6. 6.
    Woodcock J, Behrman RE, Dal Pan GJ. Role of postmarketing surveillance in contemporary medicine. Annu Rev Med. 2011;62:1–10.PubMedCrossRefGoogle Scholar
  7. 7.
    Norén GN, Hopstadius J, Bate A, Star K, Edwards IR. Temporal pattern discovery in longitudinal electronic patient records. Data Min Knowl Discov. 2010;20(3):361–87.CrossRefGoogle Scholar
  8. 8.
    Norén GN, Bate A, Hopstadius J, Star K, Edwards IR. Temporal pattern discovery for trends and transient effects: its application to patient records. In: ACM SIGKDD international conference on knowledge discovery and data mining, KDD ‘08. Las Vegas: ACM; 2008. p. 963–71.Google Scholar
  9. 9.
    Suissa S. The case–time–control design. Epidemiology. 1995;6(3):248–53.PubMedCrossRefGoogle Scholar
  10. 10.
    Ryan PB, Madigan D, Stang PE, Marc Overhage J, Racoosin JA, Hartzema AG. Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership. Stat Med. 2012;31(30):4401–15.PubMedCrossRefGoogle Scholar
  11. 11.
    Norén GN, Hopstadius J, Bate A, Edwards IR. Safety surveillance of longitudinal databases: results on real-world data. Pharmacoepidemiol Drug Saf. 2012;21(6):673–5.CrossRefGoogle Scholar
  12. 12.
    Schuemie MJ. Safety surveillance of longitudinal databases: further methodological considerations. Pharmacoepidemiol Drug Saf. 2012;21(6):670–2.PubMedCrossRefGoogle Scholar
  13. 13.
    Ryan PB, Schuemie MJ, Welebob E, Duke J, Valentine S, Hartzema AG. Defining a reference set to support methodological research in drug safety. Drug Saf. 2013 (in this supplement issue). doi:10.1007/s40264-013-0097-8.
  14. 14.
    Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55.CrossRefGoogle Scholar
  15. 15.
    Ryan PB, Schuemie MJ, Madigan D. Empirical performance of a self-controlled cohort method: lessons for developing a risk identification and analysis system. Drug Saf. 2013 (in this supplement issue). doi:10.1007/s40264-013-0101-3.
  16. 16.
    Hallas J. Evidence of depression provoked by cardiovascular medication: a prescription sequence symmetry analysis. Epidemiology. 1996;7:5.CrossRefGoogle Scholar
  17. 17.
    Farrington CP, Nash J, Miller E. Case series analysis of adverse reactions to vaccines: a comparative evaluation. Am J Epidemiol. 1996;143(11):1165–73.PubMedCrossRefGoogle Scholar
  18. 18.
    Maclure M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133(2):144–53.PubMedGoogle Scholar
  19. 19.
    Tannen RL, Weiner MG, Xie D. Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and randomised controlled trial findings. BMJ. 2009;338:b81.PubMedCrossRefGoogle Scholar
  20. 20.
    Schuemie MJ. Safety surveillance of longitudinal databases: further methodological considerations. Pharmacoepidemiol Drug Saf. 2012;21(6):670–2. doi:10.1002/pds.3259.Google Scholar
  21. 21.
    Tisdale J, Miller D. Drug-induced diseases: prevention, detection, and management. 2nd ed. USA: American Society of Health-System Pharmacists; 2010.Google Scholar
  22. 22.
    Armstrong B. A simple estimator of minimum detectable relative risk, sample size, or power in cohort studies. Am J Epidemiol. 1987;126(2):356–8.PubMedCrossRefGoogle Scholar
  23. 23.
    Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148(3):839–43.PubMedGoogle Scholar
  24. 24.
    Smith BM, Schwartzman K, Bartlett G, Menzies D. Adverse events associated with treatment of latent tuberculosis in the general population. CMAJ. 2011;183(3):E173–9.PubMedCrossRefGoogle Scholar
  25. 25.
    Carson JL, Strom BL, Duff A, Gupta A, Shaw M, Lundin FE, et al. Acute liver disease associated with erythromycins, sulfonamides, and tetracyclines. Ann Intern Med. 1993;119(7 Pt 1):576–83.PubMedCrossRefGoogle Scholar
  26. 26.
    Zorych I, Madigan D, Ryan P, Bate A. Disproportionality methods for pharmacovigilance in longitudinal observational databases. Stat Methods Med Res. 2013;22(1):39–56.PubMedCrossRefGoogle Scholar
  27. 27.
    Norén GN, Hopstadius J, Bate A. Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery. Stat Methods Med Res. 2013;22(1):57–69.PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • G. Niklas Norén
    • 1
    • 2
  • Tomas Bergvall
    • 1
  • Patrick B. Ryan
    • 3
    • 6
  • Kristina Juhlin
    • 1
  • Martijn J. Schuemie
    • 4
    • 6
  • David Madigan
    • 5
    • 6
  1. 1.Uppsala Monitoring CentreWHO Collaborating Centre for International Drug MonitoringUppsalaSweden
  2. 2.Department of MathematicsStockholm UniversityStockholmSweden
  3. 3.Janssen Research and Development LLCTitusvilleUSA
  4. 4.Department of Medical InformaticsErasmus University Medical Center RotterdamRotterdamThe Netherlands
  5. 5.Department of StatisticsColumbia UniversityNew YorkUSA
  6. 6.Observational Medical Outcomes Partnership, Foundation for the National Institutes of HealthBethesdaUSA

Personalised recommendations